The analysis of image data is becoming increasingly important in a wide variety of applications. For example, in medical applications, such as digital pathology, image analysis is routinely utilized to detect and diagnose numerous conditions. Other examples of image analysis applications include security applications involving image or face recognition, and oil and gas exploration applications involving analysis of geological images.
Unfortunately, conventional techniques for image analysis suffer from a number of significant drawbacks. For example, image analysis in many cases remains unduly human intensive. This is particularly true in digital pathology, where millions of medical images are analyzed every year by specialists and other medical professionals. These experts study medical images and try to identify patterns they have seen before. However, there is generally no mechanism available that leverages previous image analyses across multiple experts in an accurate and efficient way. Instead, the results of image analyses performed by these various experts typically remain scattered across unrelated processing systems. This also makes it very difficult to gather statistical information which could be useful in further improving the accuracy and efficiency of the image analysis process.
Existing automated techniques for image analysis are also problematic. Such techniques often demand excessive amounts of processor and memory resources, and are therefore unduly limited in terms of the number and type of image comparisons that can be performed using readily available amounts of resources.
Accordingly, a need exists for improved image data analysis techniques, which can leverage the results of previous image analyses by large numbers of distributed experts in an accurate and efficient manner, and without requiring the use of excessive amounts of processor and memory resources.